Identifying Surprising Events in Videos Using Bayesian Topic Models

  • Avishai Hendel
  • Daphna Weinshall
  • Shmuel Peleg
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6494)


Automatic processing of video data is essential in order to allow efficient access to large amounts of video content, a crucial point in such applications as video mining and surveillance. In this paper we focus on the problem of identifying interesting parts of the video. Specifically, we seek to identify atypical video events, which are the events a human user is usually looking for. To this end we employ the notion of Bayesian surprise, as defined in [1,2], in which an event is considered surprising if its occurrence leads to a large change in the probability of the world model. We propose to compute this abstract measure of surprise by first modeling a corpus of video events using the Latent Dirichlet Allocation model. Subsequently, we measure the change in the Dirichlet prior of the LDA model as a result of each video event’s occurrence. This change of the Dirichlet prior leads to a closed form expression for an event’s level of surprise, which can then be inferred directly from the observed data. We tested our algorithm on a real dataset of video data, taken by a camera observing an urban street intersection. The results demonstrate our ability to detect atypical events, such as a car making a U-turn or a person crossing an intersection diagonally.


Topic Model Latent Dirichlet Allocation Latent Topic Surprising Event Latent Dirichlet Allocation Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Avishai Hendel
    • 1
  • Daphna Weinshall
    • 1
  • Shmuel Peleg
    • 1
  1. 1.School of Computer ScienceThe Hebrew UniversityJerusalemIsrael

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